AI Visual Safety Intelligence for Manufacturing

Pick your plant, see what the cameras already show

Lines, cells and stations. Cameras already cover the floor; what is missing is anything watching them continuously.

Runs on the estate you have. RTSP and ONVIF over the existing cameras, no rip and replace.
Zones you draw. Robotic cells, electrical rooms and restricted areas as virtual boundaries.
The clip, not the shift. Event-driven recording keeps the incident, not eight hours of nothing.

Continuous plant, few people. Long stretches where nothing happens and a short window where it matters.

Fire and smoke. Detected on camera rather than waiting on a fixed-point sensor.
PPE for the hazard. Respirators, harnesses and gloves as well as helmets and vests.
Inside the boundary. Self-hosted inference where footage cannot leave the plant.

Hygiene, handling and line speed. Compliance evidence that today is a paper record and a supervisor's memory.

Evidence per event. The incident lands as a record, not a screenshot in an email.
Zone discipline. Entry to areas that require a change of gear or a wash step.
Retention that fits audit. Lifecycle policy on the footage, not manual housekeeping.

Forklifts and people sharing floor. The proximity events nobody logs until one becomes an injury.

Forklift and pedestrian. Detection of both, and of the crossings where they meet.
Near misses surface. Recurring high-risk areas visible across weeks, not one shift.
Cross-camera. The same vehicle or person followed between cameras.

Where the record is the product. What was detected, what was changed, and who saw it, all evidentially held.

Chain of custody. Incident media held with an audit trail from the moment it is captured.
De-identified for sharing. Personal data removed on the copy that leaves; original untouched.
Air-gapped where required. Full deployment with no external calls.

What You Get

What continuous safety monitoring actually takes

PPE, per zone not per plant

Helmets, vests, glasses, gloves, respirators and harnesses, with the rule set per area rather than one policy across the site.

PPE DetectionZone Rules

Hazard zones you draw

Robotic cells, electrical rooms and chemical areas as virtual boundaries, with entry raising an event the moment it happens.

Zone RulesLive AI Detection

Forklift and pedestrian proximity

Both detected, and the crossings where they meet. Recurring high-risk areas become visible across weeks rather than one shift.

Object DetectionEvent Correlation

Worker down

Falls, collapse and prolonged immobility raise an alert with the footage preserved around the event, not just a timestamp.

Activity DetectionEvent-Driven Recording

Context arrives after the alert

The detection is deterministic and immediate. A multi-modal model then describes what the scene actually showed, so the responder gets more than a class label.

Prompt-Driven Analysis

The incident becomes a record

Media is held with chain of custody and retention, so a safety investigation or a regulator request is answered from the system rather than a shared drive.

Chain of CustodyRetention

How It Works

From detection to a documented response

Detect

Computer vision runs continuously on the existing cameras and raises an event the moment a rule is met. This part is deterministic and immediate.

Understand

The event triggers a workflow. A multi-modal model reads the cached frames around it and describes what was happening, so the alert carries context rather than a bare class name.

Respond

The workflow decides what follows: notify, escalate, route to a reviewer, or hold the media as an incident record with custody intact.

FAQ

AI Visual Safety Intelligence for Manufacturing, asked and answered

What is AI visual safety intelligence?

It is continuous safety monitoring performed by AI on the cameras a plant already has, rather than by people watching screens. Computer vision detects the conditions you define -- missing PPE, entry to a hazard zone, a forklift and a pedestrian converging, a worker down -- and a workflow decides what happens next.

Does it need new cameras?

No. Detection runs over RTSP and ONVIF against the existing estate, alongside whatever VMS or NVR is already in place.

How fast is the alert?

The detection itself is real-time: computer vision reads the live stream and raises the event as the condition occurs. The added scene description that follows runs on cached frames on a cadence, so it arrives shortly after rather than in the same instant. The alert does not wait for it.

Can it explain what happened, not just what was detected?

To a degree. A multi-modal model reads the frames around the event and describes the scene in language, which is useful for situations that do not reduce to a fixed class. It is analysis of what has been happening rather than a live read of the current frame.

What happens to the footage after an alert?

Media around the event is retained as an incident record with chain of custody and a retention policy, and can be de-identified before it is shared outside the safety team.

Will it fire on a hazard that was already in progress?

No. A workflow triggers on an event, not on a state that was already true before it was watching. It sees the moment a condition is met, not a condition that pre-existed.

How accurate is it?

The response inherits the accuracy of the detection that triggered it. Confidence thresholds are configurable per detection, and a low-confidence event can be routed to a person instead of acted on automatically.

Nobody Can Watch Four Hundred Cameras. Something Has To.